Conventional Distance Sampling Research Articles

From the sky and on the beaches: complementary tools to evaluate common dolphin bycatch in the Bay of Biscay

Since 1989, multiple stranding events of common dolphins have been regularly recorded along the French Atlantic coast. Examination of the carcasses revealed that most animals presented evidence of bycatch. Using stranding data to infer bycatch levels reveals the highest levels of bycatch to have been recorded since 2016 (4000 to 9000 bycaught individuals). This approach is directly influenced by drift conditions, which can greatly contribute to or hinder our ability to estimate bycatch at sea. In the winter of 2021/2022, the French stranding network recorded an unusually high number of strandings until mid-February and few records in March. Investigation of drift conditions revealed low probability of stranding in March due to constant east-west winds. Reverse drift modelling of carcasses stranded in January and February resulted in an estimate of 3670 (95% CI [2750; 5170]) bycaught common dolphins. Dedicated aerial surveys were conducted in the same area during this period, designed to assess abundance and distribution of marine megafauna in French waters. A high number of carcasses of small Delphininae were observed in March 2021, and the number of carcasses floating at sea could be estimated using conventional distance sampling methodology. In March 2021, mortality at sea was thus estimated at 3250 (95% CI [1288;10198]) common dolphins. The complementary use of both methodologies resulted in an estimate of 6920 (95%CI [4038;15368]) bycaught individuals during winter 2021/2022. This case study highlights that a decrease in strandings does not imply a decrease in mortality at sea. Trends in strandings need to be considered in the light of scientific evidence to avoid delays in decision making.

Using fusion effects to decrease uncertainty in distance sampling models when collating data from different surveys

AbstractEstimates of population abundance are required to study the impacts of human activities on populations and assess their conservation status. Despite considerable effort to improve data collection, uncertainty around estimates of cetacean densities can remain large. A fundamental concept underlying distance sampling is the detection function. Here we focus on reducing the uncertainty in the estimation of detection function parameters in analyses combining data sets from multiple surveys, with known effects on the precision of density estimates. We developed detection functions using infinite mixture models that can be applied on data collating multiple species and/or surveys. These models enable automatic clustering by fusing the species and surveys with similar detection functions. We present a simulation analysis of a multisurvey data set in a Bayesian framework where we demonstrated that distance sampling models including fusion effects showed lower uncertainty than classical distance sampling models. We illustrated the benefits of this new model using data of line transect surveys from the Bay of Biscay and Iberian Coast. Future estimates of abundance using conventional distance sampling models on large multispecies surveys or on data sets combining multiple surveys could benefit from this new model to provide more precise density estimates.

Djaffa Mountains guereza (Colobus guereza gallarum) abundance in forests of the Ahmar Mountains, Ethiopia.

The size and density of a population are essential parameters in primate ecology and conservation. Such information, however, is still scarce for many forest primate species. The Djaffa Mountains guereza (Colobus guereza gallarum) is an endemic Ethiopian taxon for which data about its distribution and population size are missing. Therefore, we aimed to estimate the abundance and population size of the Djaffa Mountains guereza in four forests in the Ahmar Mountains southeast of the Ethiopian Rift Valley. We conducted line-transect surveys in the forests. Within an area of 183 km, we sampled 19 transects covering a distance of 75.9 km. We encountered 73 guereza clusters which most likely represent social groups. Since the detection distances and cluster sizes did not differ among the four forests, we applied a conventional distance sampling (CDS) model and estimated a population density of 20.6 clusters per square kilometer, i.e., 109.6 individuals per square kilometer or 20 061 individuals within the complete study area. This abundance is relatively high compared to other C. guereza taxa. However, given that the habitat and population of . .gallarum are already highly fragmented, further monitoring of the population and exploration of the possibilities of reconnecting its habitat should be priorities for the conservation of this taxon.

Accounting for spatial habitat and management boundaries when estimating forest bird population distribution and density: inferences from a soap film smoother.

Birds are often obligate to specific habitats which can result in study areas with complex boundaries due to sudden changes in vegetation or other features. This can result in study areas with concave arcs or that include holes of unsuitable habitat such as lakes or agricultural fields. Spatial models used to produce species' distribution and density estimates need to respect such boundaries to make informed decisions for species conservation and management. The soap film smoother is one model for complex study regions which controls the boundary behaviour, ensuring realistic values at the edges of the region. We apply the soap film smoother to account for boundary effects and compare it with thin plate regression spline (TPRS) smooth and design-based conventional distance sampling methods to produce abundance estimates from point-transect distance sampling collected data on Hawai'i 'Ākepa Loxops coccineus in the Hakalau Forest Unit of the Big Island National Wildlife Refuge Complex, Hawai'i Island, USA. The soap film smoother predicted zero or near zero densities in the northern part of the domain and two hotspots (in the southern and central parts of the domain). Along the boundary the soap film model predicted relatively high densities where 'Ākepa occur in the adjacent forest and near zero elsewhere. The design-based and soap film abundance estimates were nearly identical. The width of the soap film confidence interval was 16.5% and 0.8% wider than the width of the TPRS smooth and design-based confidence intervals, respectively. The peaks in predicted densities along the boundary indicates leakage by the TPRS smooth. We provide a discussion of the statistical methods, biological findings and management implications of applying soap film smoothers to estimate forest bird population status.

Abundance of fin whales in West Greenland in 2007

An aerial line transect survey of fin whales (Balaenoptera physalus) conducted off West Greenland in 2007 was used to estimate the current abundance of fin whales on the summer feeding ground. A total of 24 sightings of fin whale groups were collected during 8,632km of survey effort in sea states <5. Based on conventional distance sampling techniques an abundance of 4,359 whales (95% CI 1,879–10,114) was estimated. The survey was conducted as a double platform survey and mark recapture distance sampling techniques were used to correct for perception bias which resulted in an estimate of 4,468 whales (95% CI 1,343–14,871). Both estimates are negatively biased because no corrections were applied for whales that were submerged during the passage of the survey plane. The abundance estimate furthermore only represents the coastal areas of West Greenland. The sightings at the westernmost border of the strata suggest that the entire Baffin Bay-Davis Strait summer abundance of fin whales could be considerably larger. Based on comparison with previous surveys in West Greenland in 1987/88 and 2005 it appears that the fin whale abundance in West Greenland has increased.

Updated marine mammal distribution and abundance estimates in British Columbia

Information relating to the distribution and abundance of species is critical for effective conservation and management. For many species, including cetacean species of conservation concern, abundance estimates are lacking, out of date and/or highly uncertain. Systematic, line-transect marine mammal surveys were conducted in British Columbia’s (BC) coastal waters over multiple years and seasons (summer 2004, 2005, 2008, and spring/autumn 2007). In total, 10,057km of transects were surveyed in an 83,547km2 study area. Abundance estimates were calculated using two different methods: Conventional Distance Sampling (CDS) and Density Surface Modelling (DSM). CDS generates a single density estimate for each stratum, whereas DSM explicitly models spatial variation and offers potential for greater precision by incorporating environmental predictors. Although DSM yields a more relevant product for the purposes of marine spatial planning, CDS has proven to be useful in cases where there are fewer observations available for seasonal and inter-annual comparison, particularly for the scarcely observed elephant seal. The summer abundance estimates (with lower and upper 95% confidence intervals; all DSM method unless otherwise stated), assuming certain trackline detection (underestimates true population size) were: harbour porpoise (Phocoena phocoena) 8,091 (4,885–13,401); Dall’s porpoise (Phocoenoides dalli) 5,303 (4,638–6,064); Pacific white-sided dolphin (Lagenorhynchus obliquidens) 22,160 (16,522–29,721); humpback whale (Megaptera novaeangliae) 1,092 (993–1,200); fin whale (Balaenoptera physalus) 329 (274–395); killer whale (all ecotypes; Orcinus orca), 371 (222–621); common minke whale (B. acutorostrata) 522 (295–927); harbour seal (total; Phoca vitulina) 24,916 (19,666–31,569); Steller sea lion (total; Eumetopias jubatus) 4,037 (1,100–14,815); and northern elephant seal (CDS method; Mirounga angustirostris) 65 (35–121). Abundance estimates are provided on a stratum-specific basis with additional estimates provided for Steller sea lions and harbour seals that were ‘hauled out’ and ‘in water’. This analysis updates previous estimates by including additional years of effort, providing greater spatial precision with the DSM method over CDS, novel reporting for spring and autumn seasons (rather than summer alone), and providing new abundance estimates for Steller sea lion and northern elephant seal. In addition to providing a baseline of marine mammal abundance and distribution, against which future changes can be compared, this information offers the opportunity to assess the risks posed to marine mammals by existing and emerging threats, such as fisheries bycatch, ship strikes, and increased oil spill and ocean noise issues associated with increases of container ship and oil tanker traffic in British Columbia’s continental shelf waters.

A model-based estimate of winter distribution and abundance of white-tailed deer in the Adirondack Park.

In the Adirondack Park region of northern New York, USA, white-tailed deer (Odocoileus virginianus) and moose (Alces alces) co-occur along a temperate-boreal forest ecotone. In this region, moose exist as a small and vulnerable low-density population and over-browsing by white-tailed deer is known to reduce regeneration, sustainability, and health of forests. Here, we assess the distribution and abundance of white-tailed deer at a broad spatial scale relevant for deer and moose management in northern New York. We used density surface modeling (DSM) under a conventional distance sampling framework, tied to a winter aerial survey, to create a spatially explicit estimate of white-tailed deer abundance and density across a vast, northern forest region. We estimated 16,352 white-tailed deer (95% CI 11,762–22,734) throughout the Adirondack Park with local density ranging between 0.00–5.73 deer/km2. Most of the Adirondack Park (91.2%) supported white-tailed deer densities of ≤2 individuals/km2. White-tailed deer density increased with increasing proximity to anthropogenic land cover such as timber cuts, roads, and agriculture and decreased in areas with increasing elevation and days with snow cover. We conclude that climate change will be more favorable for white-tailed deer than for moose because milder winters and increased growing seasons will likely have a pronounced influence on deer abundance and distribution across the Adirondack Park. Therefore, identifying specific environmental conditions facilitating the expansion of white-tailed deer into areas with low-density moose populations can assist managers in anticipating potential changes in ungulate distribution and abundance and to develop appropriate management actions to mitigate negative consequences such as disease spread and increased competition for limiting resources.

Distance sampling surveys: using components of detection and total error to select among approaches

AbstractWildlife population estimators often require formal adjustment for imperfect detection of individuals during surveys. Conventional distance sampling (CDS) and its extensions (mark‐recapture distance sampling [MRDS], temporary emigration distance sampling [TEDS]) are popular approaches for producing unbiased estimators of wildlife abundance. However, despite extensive discussion and development of distance sampling theory in the literature, deciding which of these alternatives is most appropriate for a particular scenario can be confusing. Some of this confusion may stem from an incomplete understanding of how each approach addresses the components of the detection process. Here we describe the proper application of CDS, MRDS, and TEDS approaches and use applied examples to help clarify their differing assumptions with respect to the components of the detection process. To further aid the practitioner, we summarize the differences in a decision tree that can be used to identify cases where a more complex alternative (e.g., MRDS or TEDS) may be appropriate for a given survey application. Although the more complex approaches can account for additional sources of bias, in practical applications one also must consider estimator precision. Therefore, we also review the concept of total estimator error in the context of comparing competing methods for a given application to aid in the selection of the most appropriate distance sampling approach. Finally, we detail how the use of more advanced techniques (i.e., informed priors, open‐population distance sampling models, and integrated modeling approaches) can further reduce total estimator error by leveraging information from existing and ongoing data collection. By synthesizing the existing literature on CDS, MRDS, TEDS and their extensions, in conjunction with the concepts of total estimator error and the components of the detection process, we provide a comprehensive guide that can be used by the practitioner to more efficiently, effectively, and appropriately apply distance sampling in a variety of settings.

The Efficacy of Video Cameras to Account for Northern Bobwhites Flushed, but Undetected During Aerial Surveys

Over the past 20 years, conventional distance sampling from a helicopter platform has been used to estimate northern bobwhite (Colinus virginianus; hereafter, bobwhite) density over large areas of rangeland vegetation. However, it has been speculated that aerial surveys can complicate the ability to meet the distance sampling assumption of detecting 100% of the target objects on the transect line due to the restricted observer view from the helicopter. We attempted to use video cameras to determine whether missed detections occurred and whether digital methods could improve the precision of bobwhite density estimates. Our objectives were to 1) determine whether video cameras are a viable option to detect if coveys are flushing behind the helicopter and missed by observers, 2) determine whether coveys are flushing underneath the helicopter and missed by observers, and 3) explore the use of video cameras in a mark-recapture distance sampling (MRDS) framework. We recorded video while traversing line-transects with a helicopter during 4 distance-sampling surveys across 2 ranches in South Texas, USA. For objective 1, we reviewed footage from cameras with a backward-facing view and detected only 1 pair of bobwhites (0.001% of 889 coveys detected) that flushed on video footage recorded during the surveys but were unnoticed by observers. These results indicated that when coveys flushed, they rarely flushed behind the helicopter, and the helicopter flew at what seemed to be the proper speed and altitude to detect late flushes. For objective 2, we reviewed footage from a helicopter-mounted camera that was recorded within a swath underneath the helicopter’s center. We recorded 22 flushes within the swath, none of which was missed by the observers in the helicopter; as a result, we could not complete an MRDS analysis in Program Distance. This study improved confidence in fulfilling the assumptions of distance sampling and resulting density estimates but was limited to flushing birds only.

Extending density surface models to include multiple and double-observer survey data.

Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.

Assessment and novel application ofN‐mixture models for aerial surveys of wildlife

AbstractAerial surveys are a critical tool for inference of wildlife populations, yet are limited by current available methods to account for imperfect detection without marking animals.N‐mixture models use count data from replicate surveys for estimation of wildlife abundance while accounting for imperfect detection. This statistical framework was recently developed but is rarely applied to aerial surveys of terrestrial wildlife. We applied anN‐mixture model incorporating temporary emigration to a novel aerial survey design to estimate the availability, detection probability, and abundance of an unmarked population of white‐tailed deer (Odocoileus virginianus) in Michigan, USA, using surveys in 2014 and 2016. We assessed the selection of the sample unit size for theN‐mixture model in a post hoc sensitivity analysis and compared all estimates to a hierarchical distance sampling (HDS) approach. Hierarchical distance sampling is an expansion of conventional distance sampling (CDS), a standard method applied to aerial survey designs for wildlife, where HDS allows for modeling spatial variation in abundance and detection probability using environmental covariates and can account for changing population demographics. We foundN‐mixture models produced similar parameter estimates to HDS with both models inferring a population increase between years, which was consistent with independently available estimates for this population (2014N‐mixture = 402 [CI: 303.8–515.27], and HDS = 391 [CI: 297.67–504.63]; 2016N‐mixture = 732 [CI: 572.89–924.22], and HDS = 708 [CI: 552.45–893.89]). Estimates from both models were sensitive to the sample unit size selected in an open population framework. We recommend using ecological (e.g., home range size) and statistical (e.g., sample size) considerations when selecting a sample unit size that reflects the movement ecology of a species and evaluating the sensitivity of parameters to sample unit size. In our study,N‐mixture models and HDS models afforded a practical field approach and analytical methodology that resulted in precise estimation in a population of unmarked deer over time. When used with aerial surveys in terrestrial systems where distance sampling methods may be difficult to implement,N‐mixture models can address observer error and estimate populations for conservation and management goals without marking individual animals.

Double-observer distance sampling improves the accuracy of density estimates for a threatened arboreal mammal

Abstract Context Determining population size or density is often fundamental for wildlife conservation. For nocturnal species, indices are commonly used in place of abundance estimates, with spotlighting indices (e.g. sighting rate per km) being prevalent. Distance sampling is a collection of techniques that provide estimates of wildlife abundance from line-transect data, by correcting raw counts for imperfect detection. These methods have rarely been used to assess the abundance of nocturnal arboreal mammals. Aims To develop and evaluate a method for estimating the abundance and density of nocturnal arboreal mammals using double-observer distance sampling, and to apply the approach to a survey of the southern greater glider (Petauroides volans) in the Strathbogie Ranges, Victoria, Australia. Methods Two observers, 15–20 min apart, surveyed 25 randomly located 500 m transects, and recorded greater gliders using spotlights and binoculars. Densities and abundances were derived from the line-transect data by using mark–recapture distance sampling (MRDS) models and were compared with conventional distance sampling analysis (CDS). Key results Using the double-observer approach, we estimated an overall density of 0.96 gliders ha−1 (95% CI 0.60–1.50), giving a population estimate of 24 575 greater gliders across the Strathbogie Ranges (25 865 ha, 95% CI 15 620–38 661). The corresponding estimates for the study area derived using CDS applied to either both observers’ observations or to the first observer’s observations only, were 87% and 53% respectively, of the MRDS estimate. The analysis confirmed that the probability of detection of gliders along the transect line was less than one, justifying the use of the double-observer method to obtain accurate estimates of abundance. Conclusions The low detectability of greater gliders means that uncorrected spotlight counts will underestimate abundance, as will CDS. The double-observer method corrects for the negative bias associated with raw counts, enabling more accurate estimation of abundance for survey, monitoring and management purposes. Implications We recommend that double-observer distance sampling is adopted as a standard technique for estimating the abundance of greater gliders. The double-observer method potentially has wider relevance for assessing population size of other arboreal mammals, providing the assumptions of the approach can be met.

Mark‐Recapture Distance Sampling for Aerial Surveys of Ungulates on Rangelands

ABSTRACTAerial surveys are an efficient technique for counting animals over large geographic areas such as rangelands. In southwestern rangelands, aerial surveys are routinely conducted for ungulates, with the implicit understanding that abundance estimates represent an undercount. Distance sampling can correct for visibility bias, but assumes perfect detection on the survey line, a condition often violated in aerial surveys of ungulates. The incorporation of mark‐resight methods into the distance‐sampling framework, termed mark‐recapture distance sampling (MRDS), corrects for both visibility bias and imperfect detection on the survey line. However, the use of MRDS introduces logistical and technical constraints that may not be practical for aerial surveys. We conducted aerial surveys of ungulates on rangelands in south Texas, USA, to evaluate the feasibility and performance of MRDS relative to conventional distance sampling (CDS) and multiple covariate distance sampling (MCDS). We conducted surveys prior to and after leaf‐fall in 2013–2015 to test the hypothesis that distance sampling corrected for changes in visibility bias. We surveyed white‐tailed deer (Odocoileus virginianus), nilgai (Boselaphus tragocamelus), collared peccary (Pecari tajacu), and feral swine (Sus scrofa) on 4 sites. Each site was surveyed seasonally for 2 years; twice both prior to (Nov) and after leaf‐fall (Feb). Probability of detection on survey lines for each species was high (range = 0.82–0.97) and the average for each species was similar between seasons (0.89 and 0.92 during pre and post leaf‐fall, respectively). The MRDS density estimates often were only ~10% greater than CDS and MCDS estimates; all population estimates had overlapping 95% confidence intervals. Further, CDS and MCDS estimates were similar indicating that measured covariates (seat position, vegetation type, and cluster size) contributed little towards detection probabilities. Despite similar average probability of detection for all species before and after leaf‐fall (0.47 and 0.51, respectively), deer and nilgai population estimates were 22–59% lower during fall surveys than winter surveys. The discrepancy between consistent probability of detections with different population estimates suggests that availability bias, which cannot be addressed with distance sampling, was an issue. Overall, the MRDS technique addressed imperfect detection on the survey line and generated probabilities of detection in the survey area consistent with previous studies done in Texas. However, the extra costs (~US$13,000) and logistical hurdles to preserve observer independence for a small increase in precision of population estimates may not be justifiable. © 2020 The Wildlife Society.

Encounter rates and abundance of humpback whales (Megaptera novaeangliae) in Gerlache and Bransfield Straits, Antarctic Peninsula

During the austral summer of 2006, the Projeto Baleias/Brazilian Antarctic Program (PROANTAR) conducted ship surveys for estimating whale encounter rates and abundance in Gerlache and Bransfield Straits, westward of the Antarctic Peninsula (edge between IWC Areas I and II). The encounter rate was higher in the Bransfield Strait (0.32 groups n. mile–1; 95% CI: 0.26–0.39) than in the Gerlache Strait (0.24 groups n. mile–1; 95% CI: 0.13–0.44), though the difference was not statistically evident. An abundance estimate using conventional distance sampling methods was computed only for the Bransfield Strait. The perpendicular distance data was best fitted by the half-normal model without adjustments. Derived abundance for the surveyed area was 865 humpback whales (95% CI = 656–1,141; CV = 14.13). This area represents only a small fraction of the Stock G feeding ground.

Accurate population estimation of Caprinae using camera traps and distance sampling

With most of the world’s Caprinae taxa threatened with extinction, the IUCN appeals to the development of simple and affordable sampling methods that will produce credible abundance and distribution data for helping conserve these species inhabiting remote areas. Traditional sampling approaches, like aerial sampling or mark-capture-recapture, can generate bias by failing to meet sampling assumptions, or by incurring too much cost and logistical burden for most projects to address them. Therefore, we met the IUCN’s challenge by testing a sampling technique that leverages imagery from camera traps with conventional distance sampling, validating its operability in mountainous topography by comparing results to known abundances. Our project occurred within a captive facility housing a wild population of desert bighorn sheep (Ovis canadensis) in the Chihuahuan desert of New Mexico, which is censused yearly. True abundance was always within our 90% confidence bounds, and the mean abundance estimates were within 4.9 individuals (average) of the census values. By demonstrating the veracity of this straightforward and inexpensive sampling method, we provide confidence in its operability, urging its use to fill conservation voids for Caprinae and other data-deficient species inhabiting rugged or heavily vegetated terrain.

Distance sampling vs. plot sampling for monitoring population abundances of the Pyrenean rock ptarmigan

The rock ptarmigan (Lagopus muta) has a wide Holarctic geographical range, within which its status changes locally from threatened to abundant. For the correct management of populations under differing scenarios, accurate and precise estimates of bird abundances and/or densities are required. We used geolocated datasets from 56 counts of singing males carried out in 2003–2017 to estimate the density of Pyrenean rock ptarmigans (Lagopus muta pyrenaica). We analysed these datasets using two methodologies: (1) a plot sampling (PS) approach with two effective detection ranges (EDR) of 250 m and 350 m and (2) a conventional distance sampling (DS) approach. Our results showed that the density estimates obtained by DS were more precise and 30–87% higher than those obtained by PS using 250 m and 350 m EDR. Monitoring with PS underestimates populations and is less precise than DS; this bias is magnified when high EDR values are used. The monitoring of the Pyrenean rock ptarmigan could be significantly improved with greater use of the DS method.

Incorporating Animal Movement Into Distance Sampling

Distance sampling is a popular statistical method to estimate the density of wild animal populations. Conventional distance sampling represents animals as fixed points in space that are detected with an unknown probability that depends on the distance between the observer and the animal. Animal movement can cause substantial bias in density estimation. Methods to correct for responsive animal movement exist, but none account for nonresponsive movement independent of the observer. Here, an explicit animal movement model is incorporated into distance sampling, combining distance sampling survey data with animal telemetry data. Detection probability depends on the entire unobserved path the animal travels. The intractable integration over all possible animal paths is approximated by a hidden Markov model. A simulation study shows the method to be negligibly biased (<5%) in scenarios where conventional distance sampling overestimates abundance by up to 100%. The method is applied to line transect surveys (1999–2006) of spotted dolphins (Stenella attenuata) in the eastern tropical Pacific where abundance is shown to be positively biased by 21% on average, which can have substantial impact on the population dynamics estimated from these abundance estimates and on the choice of statistical methodology applied to future surveys. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Density and Abundance of Delphinus delphis in Waters South of Samos Island, Greece (Eastern Mediterranean Sea)

The Mediterranean subpopulation of short-beaked common dolphin Delphinus delphis is ranked as endangered on the IUCN (International Union for Conservation of Nature) Red List because it has sharply declined during the last decades, resulting in sparse and decreasing populations. Monitoring the conservation status of this endangered dolphin species is particularly relevant to fulfil targets under the range of several international agreements. Moreover, estimating the abundance of D. delphis is essential to verify the effectiveness of conservation action to maintain safe population levels in the Mediterranean Sea and to suggest appropriate modifications to limit potential threats. In this regard, a monitoring program of the short-beaked common dolphin in Samos Island (Greece) was carried out from 2016 to 2019, adopting a random line transect sampling method. The overall density and abundance estimates of D. delphis, obtained by applying conventional distance sampling (CDS) on sighting data, were 0.15 individuals/km2 (CV = 13.27%; 95% CI = 0.11–0.19 individuals/km2) and 51 individuals (CV = 13.27%; 95% CI = 40-66 individuals), respectively. Although, a longer time series of sighting data should be collected and a larger area should be investigated to better understand the population trend of D. delphis and its residency pattern, the results contribute to setting up a baseline reference for future assessment of its population in the Eastern Aegean Sea.

Modelling the effect of covariates on the detectability and density of native blackbucks and invasive feral-horse using Multiple Covariate Distance Sampling at Point Calimere Wildlife Sanctuary, Southern India

Reliable estimates of population size and a knowledge of determinants of detectability and density estimates are crucial for effective conservation of species. Using Multiple Covariate Distance Sampling (MCDS), we modelled the influence of covariates on detection probability and density estimates of native blackbuck Antilope cervicapra and the invasive feral-horse Equus caballus at Point Calimere Wildlife Sanctuary. Grids of 1 × 1 km size were overlaid on the study area with a 1-km line transect in alternate grid cells. Sixteen transects were walked four times each, which detected 199 blackbuck and 152 feral-horse clusters. On each sighting, the climatic, habitat and anthropogenic covariates were recorded, which are likely to affect detection probability. At first, exploratory analyses were made using Conventional Distance Sampling (CDS) to arrive at estimates. Later, in Multiple Covariate Distance Sampling (MCDS), key models were fit into the dataset after selecting the potential covariates that had a significant effect on detection distances obtained from FAMD/PCA, and the best one was selected based on AIC. The MCDS analysis in blackbuck included covariates, viz., distance to water in the best model (29 individuals/km2; CI 22–36; detection probability = 0.58), followed by distance to feral-horse, sampling time and principal diet. The covariate distance to water emerged as the best model for feral-horse as well (13 individuals/km2; CI 9–18; detection probability = 0.36), followed by distance to cattle and principal diet. While the MCDS approach outperformed the CDS global and survey-strata estimates in blackbuck, both the approaches had a marginal difference in feral-horse. Post-stratification analysis showed that blackbuck density increased significantly with distance to water and feral-horse, but insignificantly with the absence of principal diet; whereas, the density of the feral-horse increased significantly with distance to water and cattle in the presence of principal diet. These findings suggest the ability of feral-horse to keep the blackbuck away from optimal areas, where the principal diet is abundant. Our study, thus, illustrates the need for the use of MCDS approach that ascertained (i) reliable population density estimates, (ii) spatiotemporal constraint on large herbivores caused by overabundance of water during the wet season, and (iii) competitive interaction of the invasive feral-horse with the native blackbuck and its likely effect on the latter keeping away from the former and the principal diet to overcome competition. Thus, the study highlights the impact of feral-horse on the native species and suggests measures for the long-term conservation of the blackbuck.

Spatially explicit density and its determinants for Asiatic lions in the Gir forests.

Asiatic lions (Panthera leo persica) are an icon of conservation success, yet their status is inferred from total counts that cannot account for detection bias and double counts. With an effort of 4,797 km in 725 km2 of western Gir Protected Area, India, we used polygon search based spatially explicit capture recapture framework to estimate lion density. Using vibrissae patterns and permanent body marks we identified 67 lions from 368 lion sightings. We conducted distance sampling on 35 transects with an effort of 101.5 km to estimate spatial prey density using generalized additive modeling (GAM). Subsequently, we modeled lion spatial density with prey, habitat characteristics, anthropogenic factors and distance to baiting sites. Lion density (>1-year-old lions) was estimated at 8.53 (SE 1.05) /100 km2 with lionesses having smaller movement parameter (σ = 2.55 km; SE 0.12) compared to males (σ = 5.32 km; SE 0.33). Detection corrected sex ratio (female:male lions) was 1.14 (SE 0.02). Chital (Axis axis) was the most abundant ungulate with a density of 63.29 (SE 10.14) as determined by conventional distance sampling (CDS) and 58.17 (SE 22.17)/km2 with density surface modeling (DSM), followed by sambar (Rusa unicolor) at 3.84 (SE 1.07) and 4.73 (SE 1.48)/km2 estimated by CDS and DSM respectively. Spatial lion density was best explained by proximity to baiting sites and flat valley habitat but not as much by prey density. We demonstrate a scientifically robust approach to estimate lion abundance, that due to its spatial context, can be useful for management of habitat and human-lion interface. We recommend this method for lion population assessment across their range. High lion densities in western Gir were correlated with baiting. The management practice of attracting lions for tourism can perturb natural lion densities, disrupt behavior, lion social dynamics and have detrimental effects on local prey densities.